Abstract
This paper proposes a surrogate-driven multi-objective predictive control (SMPC) strategy to address the dynamics uncertainty and multi-objective optimization issues of electric vehicular platoon (EVP). A surrogate-driven model is established with subspace identification to alleviate the adverse effects of uncertain dynamics for EVP. Then, a subspace predictor-based distributed surrogate-driven model predictive controller is developed for EVP. To mitigate conflicts among multiple optimization objectives involving driving safety, driving comfort and energy economy, a multi-objective cost function with the predictive sequence is designed. To this end, a grey wolf optimizer is suggested to guide the search towards diverse solutions, aiming to achieve globally optimal trade-offs among conflicting multiple objectives. In this way, the SMPC strategy is constructed, and its stability is theoretically proven. Finally, several experiments are carried out on a co-simulation vehicular platoon platform with the IPG-CarMaker software. The experimental results validate the effectiveness of the proposed SMPC strategy.
Original language | English |
---|---|
Article number | 10476631 |
Journal | IEEE Transactions on Transportation Electrification |
Early online date | 20 Mar 2024 |
DOIs | |
Publication status | E-pub ahead of print - 20 Mar 2024 |
Bibliographical note
Funding:This work was supported by the National Natural Science Foundation of China under Grant 62173243, Grant 61933014 and Grant 61803218, in part by the Open Research Program of the Key Laboratory of System Control and Information Processing, Ministry of Education, under Grant Scip202101.
Keywords
- Optimization
- Predictive models
- Vehicle dynamics
- Mathematical models
- State of charge
- Batteries
- Transportation